Not surprisingly, everywhere you look, software companies are adopting the terms “visual analytics” and “interactive data visualization.” Tools that do little more than produce charts and dashboards are now laying claim to the label.
Let’s start with what visual analysis is not: A graphical depiction of data. Virtually any software application can produce a chart, gauge or dashboard. Visual analytics offers something much more profound. Visual analytics is the process of analytical reasoning facilitated by interactive visual interfaces.
Who is adopting visual analytics?
Visual analytics is being adopted by the world’s leading companies, universities and government agencies. From the world’s largest and most innovative organizations – Proctor & Gamble, Apple, Pfizer, Microsoft, Coca Cola, Google, Cornell University, Progressive Insurance, Amazon, Georgetown University, the VA (Veteran’s Administration), Blue Cross Blue Shield – to one-person consulting shops, visual analysis tools are now mainstream.
No aspiring painter would put up with a paint-by numbers canvas. But that’s what many programs force on people when they use charting wizards and dashboards. Good visual analytics tools accommodate people’s need for depth, flexibility and expressiveness in the visual displays. This is especially important when people need to look at more than two or three dimensions of a problem simultaneously. Imagine putting five dimensions of a problem (e.g., Year, Month, Region, Product Family and Units Sold) into a charting wizard: the result just doesn’t come out well. Visual analytics applications let people visualize multiple dimensions of a problem effortlessly, in formats that are easy to understand. Where crosstabs and pivot-tables often confuse and overwhelm, multi-dimensional visualizations clarify. Visual analytics applications display complex problems with elegant simplicity.
This makes me share our recent solution that helped a global pharmaceutical firm in transforming their data into metrics-driven executive dashboards that provide the depth and dimension required to measure business performance. Using Tableau, an advanced visualization tool, we built dashboards that combine data from internal and external sources in the same view, and allow their key management personnel get a comprehensive view of their business.
Imagine an application that tells you how you should look at the specific problem you have. For too long, analysts have been taught to think in numbers alone. A visual analytics application jumpstarts the analysis process itself. This includes automatically suggesting effective visualizations. A key benefit of automatic visualization is not just that it reduces work time. It also helps people learn to think visually. If they can think in pictures, they can work faster and recall trends and patterns more easily.
Some of the most common and useful interactions in data analysis include:
- Filtering out what’s not relevant
- Sorting the data to see it in order of magnitude
- Moving between high-level (the big picture) and low-level (the details) views of the data
- Drilling up and down through levels in hierarchically structured data
- Changing your view of the data, such as by switching to a different type of graph, to view it from a different perspective
Good visual analysis software provides every means to interact with the data that you frequently need (filtering, sorting, etc.), and allows you to perform each action conveniently, without losing sight of the data. And finally, good visual analysis software makes it easy to navigate through the analytical process, from view to view, interaction to interaction, overview to detail, riding the wave of thought smoothly throughout the process.